Given a table of new parameter values with a new parameter set per line, runs iteratively Markov models over these sets.
# S3 method for run_model update(object, newdata, ...) # S3 method for updated_model plot( x, type = c("simple", "difference", "counts", "ce", "values"), result = c("cost", "effect", "icer"), strategy = NULL, ... )
| object | The result of |
|---|---|
| newdata | A |
| ... | Additional arguments passed to
|
| x | Updated model to plot. |
| type | Plot simple values or differences? |
| result | The the result to plot (see details). |
| strategy | A model index, character or numeric. |
A data.frame with one row per model/value.
newdata must be a data.frame with the
following properties: the column names must be parameter
names used in define_parameters(); and an
optional column .weights can give the respective
weight of each row in the target population.
Weights are automatillcally scaled. If no weights are provided equal weights are used for each strata.
For the plotting function, the type argument can
take the following values: "cost", "effect"
or "icer" to plot the heterogeneity of the
respective values. Furthermore "ce" and
"count" can produce from the combined model plots
similar to those of run_model().
Histograms do not account for weights. On the other hand summary results do.
mod1 <- define_strategy( transition = define_transition( .5, .5, .1, .9 ), define_state( cost = 543 + age * 5, ly = 1 ), define_state( cost = 432 + age, ly = 1 * age / 100 ) )#>#>mod2 <- define_strategy( transition = define_transition( .5, .5, .1, .9 ), define_state( cost = 789 * age / 10, ly = 1 ), define_state( cost = 456 * age / 10, ly = 1 * age / 200 ) )#>#>res <- run_model( mod1, mod2, parameters = define_parameters( age_init = 60, age = age_init + markov_cycle ), init = 1:0, cycles = 10, cost = cost, effect = ly )#># generating table with new parameter sets new_tab <- data.frame( age_init = 40:45 ) # with run_model result ndt <- update(res, newdata = new_tab)#>#>#>#> An analysis re-run on 6 parameter sets. #> #> * Unweighted analysis. #> #> * Values distribution: #> #> Min. 1st Qu. Median Mean #> II - Cost 24622.032937 25301.872444 25981.711950 25981.711950 #> II - Effect 4.332100 4.378108 4.424115 4.424115 #> II - Cost Diff. - - - - #> II - Effect Diff. - - - - #> II - Icer - - - - #> I - Cost 5533.254653 5558.948587 5584.642522 5584.642522 #> I - Effect 6.025414 6.117429 6.209444 6.209444 #> I - Cost Diff. -21705.360572 -21051.215000 -20397.069428 -20397.069428 #> I - Effect Diff. 1.693313 1.739321 1.785329 1.785329 #> I - Icer -11561.740443 -11494.795100 -11424.530031 -11421.356676 #> 3rd Qu. Max. #> II - Cost 26661.551457 27341.390963 #> II - Effect 4.470123 4.516131 #> II - Cost Diff. - - #> II - Effect Diff. - - #> II - Icer - - #> I - Cost 5610.336457 5636.030391 #> I - Effect 6.301459 6.393474 #> I - Cost Diff. -19742.923856 -19088.778285 #> I - Effect Diff. 1.831336 1.877344 #> I - Icer -11350.699496 -11273.033445 #> #> * Combined result: #> #> 2 strategies run for 10 cycles. #> #> Initial state counts: #> #> A = 1L #> B = 0L #> #> Counting method: 'life-table'. #> #> #> #> Counting method: 'beginning'. #> #> #> #> Counting method: 'end'. #> #> Values: #> #> cost ly #> I 5584.643 6.209444 #> II 25981.712 4.424115 #> #> Efficiency frontier: #> #> I #> #> Differences: #> #> Cost Diff. Effect Diff. ICER Ref. #> I -20397.07 1.785329 -11424.83 II# using weights new_tab2 <- data.frame( age_init = 40:45, .weights = runif(6) ) ndt2 <- update(res, newdata = new_tab2)#>#>#> An analysis re-run on 6 parameter sets. #> #> * Weigths distribution: #> #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.01953 0.16782 0.31989 0.35622 0.46751 0.84416 #> #> Total weight: 2.137309 #> #> * Values distribution: #> #> Min. 1st Qu. Median Mean #> II - Cost 24622.032937 25095.199985 25890.720452 25580.831389 #> II - Effect 4.332100 4.364122 4.417958 4.396986 #> II - Cost Diff. - - - - #> II - Effect Diff. - - - - #> II - Icer - - - - #> I - Cost 5533.254653 5551.137584 5581.203579 5569.491595 #> I - Effect 6.025414 6.089456 6.197128 6.155185 #> I - Cost Diff. -21705.360572 -20951.480377 -20309.516874 -20011.339794 #> I - Effect Diff. 1.693313 1.725335 1.779171 1.758199 #> I - Icer -11561.740443 -11484.257702 -11414.894343 -11378.357105 #> 3rd Qu. Max. #> II - Cost 26557.899395 27341.390963 #> II - Effect 4.463108 4.516131 #> II - Cost Diff. - - #> II - Effect Diff. - - #> II - Icer - - #> I - Cost 5606.419018 5636.030391 #> I - Effect 6.287430 6.393474 #> I - Cost Diff. -19544.062401 -19088.778285 #> I - Effect Diff. 1.824322 1.877344 #> I - Icer -11327.543060 -11273.033445 #> #> * Combined result: #> #> 2 strategies run for 10 cycles. #> #> Initial state counts: #> #> A = 1L #> B = 0L #> #> Counting method: 'life-table'. #> #> #> #> Counting method: 'beginning'. #> #> #> #> Counting method: 'end'. #> #> Values: #> #> cost ly #> I 5569.492 6.155185 #> II 25580.831 4.396986 #> #> Efficiency frontier: #> #> I #> #> Differences: #> #> Cost Diff. Effect Diff. ICER Ref. #> I -20011.34 1.758199 -11381.72 II